Observer-based Consensus for Second-order Nonlinear Multi-agent Systems via Adaptive Repetitive Learning Control

In this paper, we consider the consensus tracking for second-order multi-agent systems via adaptive repetitive learning control. Under the assumption that the velocity information may be unmeasured for any of the following agents, an observer-based distributed adaptive repetitive learning consensus protocol is designed. Based-on the Lyapunov stability theory, a sufficient condition which guarantees the following agents tracking the leader uniformly in the finite time interval is derived. Finally, simulation results are given to illustrate the performance and effectiveness of our proposed protocols in this article.